Background: Physical frailty is associated with social activity. However, the relationship between physical frailty and levels of engagement with other people during social activities remains unclear. Thus, we aimed to clarify the relationship between physical frailty and social activity using a taxonomy of activity levels among community-dwelling older adults in Japan. Methods: This cross-sectional observational study analyzed data from 12,788 older adults (7001 women, mean age: 73.8 years, standard deviation = 5.9; range: 60–96 years) from the National Center for Geriatrics and Gerontology-Study of Geriatric Syndromes. Physical frailty was assessed using the following components: slow walking speed, muscle weakness, exhaustion, low activity, and weight loss. We asked participants about seven social activities that included social participation and engagement and examined their relationship to physical frailty. Results: Physical frailty was independently associated with all social activities. Exercise circle activity, which includes a level of social participation, was strongly associated with physical pre-frailty and physical frailty. Results of sub-analyses indicated that the level of social engagement was independently associated with physical frailty in the older group (over 75 years) but not in the younger group (60–74 years). Conclusions: Our results indicate that the strength of the association between social activity and physical frailty differs by the level of social participation. Given the increasingly high prevalence of physical frailty in Japan and its strong association with numerous adverse health outcomes, the relationship between physical frailty and levels of social participation may assist in developing measures to prevent the incidence and progression of physical frailty.

The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.

The aim of this study was to examine the association between lifestyle activities, including physical, cognitive, and social activities, and Alzheimer’s disease (AD) signature cortical thickness, as well as to examine the mediating role of AD signature cortical thickness in lifestyle activities and cognitive function in community-dwelling healthy older adults. Participants were 1026 older adults who met the study inclusion criteria. The physical, cognitive, and social activities of daily life were assessed using a self-reporting questionnaire. AD signature cortical thickness was determined using FreeSurfer software. Cognitive function was evaluated using the National Center for Geriatrics and Gerontology-Functional Assessment Tool. Path analysis (based on structural equation modeling (SEM)) of cognitive activities indicated that the direct path from cognitive activities to cognitive function was significant (p < 0.001), as was the direct path from AD signature cortical thickness to cognitive function (p < 0.001). Physical (p < 0.05) or social activities (p < 0.05) had a direct effect on cognitive function. However, AD signature cortical thickness did not mediate the relationship between physical or social activities and cognitive function. Our findings suggest that higher levels of cognitive activities later in life have a significant and positive direct effect on cognitive function. Additionally, AD signature cortical thickness significantly mediates the relationship between cognitive activities and cognitive function.